Ethanol physiological dependence and associated withdrawal episodes are thought to constitute a motivational force that perpetuates ethanol use/abuse and contributes to relapse. In humans, the identification of genes that influence ethanol physical dependence and withdrawal has been limited. Thus, the use of preclinical (animal) models that closely approximate the clinical situation is essential to elucidate the genes and gene networks involved. Quantitative trait loci (QTLs) are chromosome sites containing alleles (genes) that influence a quantitative (complex) trait such as predisposition to ethanol physiological dependence and associated withdrawal. Our analyses identify with high certainty (LOD>7.6, p<2x10[-9]) QTLs on chromosome 1 that affect withdrawal after both acute and chronic ethanol exposure in mice. Component #6 is focused on a distal chromosome 1 QTL that, during the current funding period, we fine-mapped to a 0.44 Mb interval (syntenic with human Chr 1q23.2). We identified high-quality quantitative trait gene (QTG) candidates and developed a novel knockout (KO) genetic animal model for a particularly promising QTG candidate, Kcnj9. As expected this Kcnj9 KO mice exhibited significantly less severe withdrawal from ethanol as well as pentobarbital and Zolpidem. Kcnj9 encodes GIRK3 (Kir3.3), a subunit member of a family of G-protein-dependent inwardly-rectifying K[+] (GIRK) channels. Component #6 seeks to identify the QTG(s) underlying this chromosome 1 QTL, and assess its potential role in genetically correlated behaviors. We propose the following three aims: (1) Compare Kcnj9 KO and wildtype llttermates for behaviors that are genetically correlated with acute ethanol withdrawal and Kcnj9 expression (i.e., ethanol preference drinking, chronic withdrawal, and impulsivity) as well as withdrawal-induced drinking. (2) Using RNA interference (RNAi), rigorously test the hypothesis that reduced Kcnj9 expression has a causal role to reduce ethanol withdrawal severity. (3) Use available approaches to provide mechanistic information at every opportunity. This will include weighted gene co-expression network analysis (WGCNA) using genetically modified (e.g., Kcnj9 KO or chromosome 1 congenic) and wildtype llttermates. An innovative feature of this proposal is to combine robust behavioral models of ethanol withdrawal with state-of-the-art strategies to elucidate the QTG and its mechanism of action. The proposed work will complete the journey from identifying genetic risk due to anonymous genes to identification of a QTG(s) that confers risk for ethanol withdrawal. This will set the stage for future translational and mechanistic studies.